🤖 AI Summary
This work addresses the inadequacy of current AI runtime logs in providing the structured evidence necessary for legal fact-finding—such as data boundary violations or human interventions. It formalizes, for the first time, the binary factual requirements of regulatory compliance into a criterion of evidentiary sufficiency for runtime records, mandating that logs explicitly encode the legal category of events and their determinative relationships (e.g., provenance, authorization, temporal validity). By integrating legal ontologies, event-type systems, provenance semantics, and temporal validity constraints—and drawing on the law of requisite variety and the Good Regulator theorem from cybernetics—the approach exposes limitations in tamper-proof logging and generic provenance mechanisms. Validation against selected obligations of the EU AI Act demonstrates that this criterion precisely delineates the boundary between traces and hyperproperties in runtime verification, thereby establishing a verifiable foundation for compliance.
📝 Abstract
Agentic AI systems generate runtime records, logs, traces, and audit artefacts, but the existence or integrity of such records does not by itself establish that legally operative oversight findings can be recovered from them. This technical report defines an evidentiary-adequacy criterion for a bounded class of determinations: binary findings of fact about specific events and their relations, such as whether protected data crossed a boundary, whether a human could intervene, whether an information barrier held, or whether delegated authority was valid at the moment of use.
The criterion states that a runtime record can answer such a determination only if it carries both a typing that maps recorded events to the legally operative category and the relation, such as provenance, authority, derivation, or temporal validity, on which the determination's truth depends. The claim is one of necessity, not sufficiency.
The report instantiates the criterion against selected EU AI Act oversight obligations and explains why tamper-proof logs, generic process frameworks, and provenance structures alone cannot establish the relevant findings. It further relates the argument to requisite variety, the Good Regulator Theorem, and the trace-versus-hyperproperty boundary of runtime verification. Companion materials and the experiment protocol are archived on Zenodo.